Research on DSC-MB-PSPNet Semantic Segmentation inStreet-scene Autonomous Driving
It presented a lightweight real-time semantic segmentation model for city autonomous driving. A deep separable convolution, multi-branch and Pyramid scene parsing network fusion structure (DSC-MB-PSPNet) was proposed to ensure the model has better characterization ability and real-time operation, an...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2020-01-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2020.04.100 |
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| Summary: | It presented a lightweight real-time semantic segmentation model for city autonomous driving. A deep separable convolution, multi-branch and Pyramid scene parsing network fusion structure (DSC-MB-PSPNet) was proposed to ensure the model has better characterization ability and real-time operation, and inhibitory cross entropy loss function was presented to reduce the impact of sample imbalance and eliminate the serious imbalance of pixel number among samples in street scenes autonomous driving. At the same time, in order to improve the training efficiency and make the model easier to converge, the multi-stage loss function calculation method was added in the model. DSC-MB-PSPNet was evaluated on the Cityscapes and self-built datasets. The results show that under the condition of large resolution input, the proposed model achieves state-of-the-art performance and can work in real-time. |
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| ISSN: | 2096-5427 |